2025Schafer CryoSift

Citation

Schäfer, J.-H., Calza, A., Hom, K., Damodar, P., Peng, R., Bogdanović, N., Lander, G.C., Stagg, S.M. and Cianfrocco, M.A. 2025. CryoSift: an accessible and automated CNN-driven tool for cryo-EM 2D class selection. Acta Crystallographica Sec. F. 81, 12 (2025), 517–526.

Abstract

Single-particle cryo-electron microscopy (cryo-EM) has become an essential tool in structural biology. However, automating repetitive tasks remains an ongoing challenge in cryo-EM data-set processing. Here, we present a platformindependent convolutional neural network (CNN) tool for assessing the quality of 2D averages to enable the automatic selection of suitable particles for highresolution reconstructions, termed CryoSift. We integrate CryoSift into a fully automated processing pipeline using the existing cryosparc-tools library. Our integrated and customizable 2D assessment workflow enables high-throughput processing that accommodates experienced to novice cryo-EM users.

Links

https://journals.iucr.org/f/issues/2025/12/00/ih5009/index.html